Tutorial sull’IA

This is a simple guide to fine-tuning Gemma 2 9B-It on patient-doctor conversations and converting the model into GGUF format so that it can be used locally with the Jan application.

Get started with the new Llama models and customize Llama-3.1-8B-It to predict various mental health disorders from the text.

ScrapeGraph AI is an open-source tool that simplifies web scraping by automatically extracting structured data from websites, allowing users to interact with and retrieve the data through simple prompts.

Zero-shot prompting is a technique in which an AI model is given a task or question without any prior examples or specific training on that task, relying solely on its pre-existing knowledge to generate a response.

Few-shot prompting is a technique in which an AI model is given a few examples of a task to learn from before generating a response, using those examples to improve its performance on similar tasks.

Learn how to convert technical models into interactive user interfaces with Gradio in Python.

Learn the differences between RAG and Fine-Tuning techniques for customizing model performance and reducing hallucinations in LLMs.

Qwen is a family of large language and multimodal models developed by Alibaba Cloud, designed for various tasks like text generation, image understanding, and conversation.

Unlock the full potential of Large Language Models (LLMs) with our guide on Chain-of-Thought (CoT) prompting. Learn how to enhance reasoning and problem-solving abilities in LLMs.

This article compares the performance of MatMul-free and traditional large language models in terms of speed and memory usage.

This article presents a practical guide to implementing TNT-LLM, a framework that automates taxonomy generation and text classification using LLMs, demonstrated through a news article clustering example with GPT-4 and LangGraph.

Prompt chaining is a technique that involves breaking down a complex task into a series of smaller, interconnected prompts, where the output of one prompt serves as the input for the next, guiding the LLM through a structured reasoning process.